Ontologies as Strategy to Represent Knowledge Audit

Ontologies as Strategy to Represent Knowledge
Audit Outcomes
Alonso Perez-Soltero
Gerardo Sanchez-Schmitz
Mario Barcelo-Valenzuela
Jose Tomas Palma-Mendez
Fernando Martin-Rubio
VOLUME 2, NUMBER 5
INTERNATIONAL JOURNAL OF TECHNOLOGY, KNOWLEDGE AND SOCIETY
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Ontologies as Strategy to Represent Knowledge Audit Outcomes
Alonso Perez-Soltero, University of Sonora, Mexico
Gerardo Sanchez-Schmitz, University of Sonora, Mexico
Mario Barcelo-Valenzuela, University of Sonora, Mexico
Jose Tomas Palma-Mendez, University of Murcia, Spain
Fernando Martin-Rubio, University of Murcia, Spain
Abstract: Normally, after applying a knowledge audit methodology, the results are presented in a final report including
knowledge inventory, knowledge maps, and knowledge flows. After analyzed the inventory, maps and flows, it is possible
to identify inefficiencies reflected in duplication of efforts, knowledge gaps, knowledge barriers and knowledge-bottlenecks.
All this information is integrated at the final report and is presented to managers, including diverse knowledge management
initiatives. The main problems of representing the knowledge audit results only in this way, are the inefficiency of searching
specific information about a knowledge asset; and difficulty of reuse them if a technological solution is needed as a part of
a knowledge management initiative. The aim of this paper is to demonstrate the importance of using ontologies as a strategy
to represent formally knowledge audit results to solve the previous problems and additionally obtain the next benefits: A
support tool to detect problems/opportunities found in the organization to improve knowledge management; the results of
the audit can be reused if a technological solution is needed; as a source of reference to know what, where, characteristics,
classification and value of any assets of knowledge; as a form to represent the flow and its relation with the rest of assets;
an efficient way to retrieve information from the inventory and/or flows of knowledge and automatically to know the impact
and relation with the rest of the knowledge assets.
Keywords: Knowledge Audit, Ontologies, Knowledge Management, Knowledge Map, Knowledge Flow, Knowledge Inventory
Introduction
ANY ORGANIZATIONS ARE familiar
with managing their operations, marketing, finance, sales or even supply chain.
However, it is far from adequate for them
to win in the very dynamic and highly competitive
markets nowadays (Cheung et al., 2005). Increasingly
the knowledge and skills of employees are seen as
valuable assets that may be utilized in order to gain
competitive advantage at an organizational level
(Burnett, 2004). It is known that if knowledge is
managed well, organizations can leverage on their
knowledge, internal and external, for creation of new
knowledge and innovation. It thus helps them to
create values to the organizations (Cheung et al.,
2005).
There are challenges related to knowledge management (KM) into organizations. One challenge is
associated to knowledge auditing. In this case,
knowledge audit methodologies do not establish a
clear strategy explaining a suitable place where the
knowledge audit in a enterprise or area should be
initiated to give an order to complete the audit, in
other words, they attempt to audit everything, significant or not to the organization. Other deficiencies
found in the great majority of the knowledge audit
M
methodologies, is that they do not establish measurement criteria to verify the impact related to KM
processes. The methodologies analyzed need to be
completed applied to detect problems/opportunities
and then propose some improvements to the organization in relation to KM (Perez-Soltero et al., 2006).
Other aspect refers to knowledge audit outcomes.
After applying a knowledge audit, the results are
presented in a final report including knowledge inventory, knowledge maps, and knowledge flows. All
this information is integrated at the final report and
is presented to managers, including diverse KM initiatives. The knowledge audit results represented
only of this form has some disadvantages as: inefficiency of searching specific information about a
knowledge asset; and difficulty of reuse them if a
technological solution is needed as a part of a
knowledge management initiative. Other of the main
challenge is related to appropriate knowledge representation to manage knowledge efficiently within an
organization.
The aim of this paper is to demonstrate the importance of using ontologies as a strategy to represent
formally knowledge audit results to improve the efficiency of searching specific information about
knowledge assets; and facilitate knowledge reuse if
INTERNATIONAL JOURNAL OF TECHNOLOGY, KNOWLEDGE AND SOCIETY, VOLUME 2, NUMBER 5, 2006
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INTERNATIONAL JOURNAL OF TECHNOLOGY, KNOWLEDGE AND SOCIETY, VOLUME 2
a technological solution is needed as a part of a KM
initiative.
The structure of this paper first describes some
concepts related to knowledge in organizations,
knowledge audit, knowledge audit methodologies
and ontologies. Secondly, the importance of using
ontologies to support knowledge audit outcomes and
main aspects to consider in an ontology-based
framework are analized. Finally, a discussion, conclusion and future work are explained.
Conceptual Framework
Some of the main topics related to knowledge in organizations, knowledge audit, knowledge audit
methodologies and ontologies are explained in this
section.
Knowledge in Organizations
It is very common the distinction between ‘tacit
knowledge’ and ‘explicit knowledge.’ As (Polanyi,
1967) put it, ‘We can know more than we can tell’.
This phrase was used to describe tacit knowledge.
Tacit knowledge is the knowledge that a person
posses and that it is described as knowledge embedded in the individual’s experience and it has a personal quality, which makes it hard to formalize and
communicate. In his words, it ‘indwells’ in a comprehensive cognizance of the human mind and body.
This experience can be communicated and exchanged
in a direct and effective way in the socialization
process (Nonaka and Takeuchi, 1995). The explicit
knowledge refers to the knowledge that is transferable in a formal and systematic way, by means of a
language, since it can be easily articulated and interchanged, because it is independent of the individual's
mind. (Gualtieri and Ruffolo, 2005) additionally explain that explicit knowledge can also be classified
based on the following forms: "structured" (available
in database), “semistructured" (available in intranet
and internet web sites: HTML pages, XML documents, etc.) and "unstructured" (available as textual
documents: project documents, procedures, white
papers, templates, etc.)
Another particular classification establishes a
separation among the declarative, procedural and
heuristic knowledge (Vasconcelos et al., 2000). Declarative knowledge is related with the physical aspects of the knowledge and responds to the questions:
What? Who? Where? and When?. It is a knowledge
that serves to describe specific actions to perform
certain tasks. Procedural knowledge describes actions
for the following step and responds to the question:
How? Finally, Heuristic knowledge describes the
implicit reasoning and the individual’s experience.
This knowledge uses declarative and procedural
knowledge to solve problems and there for to answer
the question Why?
Knowledge Audit
Many of the mistakes of both, earlier and more recent
adopters of KM can be traced to the serious oversight
of not including the knowledge audit in their overall
KM strategies and initiatives (Hylton, 2002b). A
knowledge audit (an assessment of the way knowledge processes meet an organization’s knowledge
goals) is to understand the processes that constitute
the activities of a knowledge worker, and see how
well they address the “knowledge goals” of the organization (Lauer and Tanniru, 2001). Liebowitz
defines a knowledge audit as a tool that assets potential stores of knowledge. It is the first part of any
KM strategy. By discovering that knowledge is possessed, it is then possible to find the most effective
method of storage and dissemination. It can then be
used as the basis for evaluating the extent where
change needs to be introduced to enterprise. Part of
the knowledge audit is capturing “tacit” knowledge
(Liebowitz et al., 2000).
The knowledge audit is used to provide a sound
investigation into the organization’s knowledge
“health”. The knowledge audit is a discovery, verification and validation tool, providing fact-finding,
analysis, interpretation, and reports. It includes a
study of corporate information and knowledge
policies and practices, of its information and knowledge structure and flow. The knowledge audit examines knowledge sources and use: how and why
knowledge is acquired, accessed, disseminated,
shared and used. The knowledge audit will seek to
give qualified insight as to whether the organization
is ready, especially socially and politically, to become knowledge-based or knowledge-centred
(Hylton, 2002b).
S. Capshaw believes that a knowledge audit should
provide the following outputs: an assessment of
current levels of knowledge usage and interchange;
knowledge management propensity within the enterprise; identification and analysis of knowledge
management opportunities; isolation of potential
problem areas; and an evaluation of the perceived
value in knowledge within the enterprise (Capshaw,
1999).
Knowledge audit is the indisputable first major
step or stage in a KM initiative (Burnet et al., 2004),
(Henczel, 2000), (Hylton, 2002b), yet it has not been
sufficiently recognized as being of supreme importance to every KM undertaking. To effectively design
the KM systems both the organizational knowledge
and the KM functions must be individuated by conducting the knowledge audit of the same organization, as these are needed to perform the business
processes (Iazzolino and Pietrantonio, 2005).
PEREZ-SOLTERO, SANCHEZ-SCHMITZ, BARCELO-VALENZUELA, PALMA-MENDEZ, MARTIN-RUBIO
Knowledge Audit Methodologies
According to (Robertson, 2002) there are many benefits in applying a KM framework or methodology:
offers legitimacy, provides consistent language,
outlines a process, provides a checklist, offers a
source of ideas and addresses non-technical aspects.
Gartner Group contends, for example, that a
“knowledge audit” needs to be undertaken during
the initial stages of the KM program. They state: The
audit should identify the knowledge requirements of
all processes that are heavily dependent on intellectual assets and that underlie the targeted business
objectives. The audit ought to identify knowledge
sources that can fulfil these knowledge requirements
and the high-level business process steps where that
knowledge must be applied (Gartner Group, 2000).
Company executives would do well to give serious
consideration to undertaking a knowledge audit –
even a small one. It is perfectly acceptable, and
highly recommended that an organization begins a
corporate knowledge audit by auditing one small
team, unit, department, or a business process (Hylton,
2002a).
A knowledge audit will consist of two major tasks,
each of which can be done without the other. The
first, often called knowledge mapping, involves locating repositories of knowledge throughout the organization. This effort is primarily technological and
usually prepares the way for creating a knowledge
database. The knowledge mapping process is relatively straightforward. It takes an inventory of what
people in the organization have written down or
entered into information systems, as well as identifying sources of information employees use that come
from the outside (such as public or university libraries, Web sites or subscription services). Finding and
organizing all that data may be time-consuming, but
it is not conceptually difficult. The second, a more
intensive category of audit task attempts to capture
the patterns of knowledge flow in the organization.
This knowledge flow audit examines how people
process information that ultimately determines how
well an organization uses and shares its knowledge
(Stevens, 2000).
While there seem to be several ways of conducting
a knowledge audit (Liebowitz et al., 2000), (Henczel,
2000), (Lauer & Tannuri, 2001), (Housel and
Kanevsky, 2001), (Hylton, 2002c), (Skyrme, 2002),
(Schwikkard & du Toit, 2004), (Burnet et al., 2004),
(Choy et al., 2004), (Iazzolino & Pietrantonio, 2005),
(Cheung et al, 2005), in general knowledge audits
consist of: the identification of knowledge needs
through the use of questionnaires, interviews and
focus groups; the development of a knowledge inventory mainly focusing on the types of knowledge
available; where this knowledge is located; how it
is maintained and stored, what it is used for and how
relevant it is; analysis of knowledge flows in terms
of people, processes and systems, the creation of a
knowledge map; finally an audit detailed report.
Ontologies
An ontology, is a shared, formal conceptualization
of a domain (Gruber, 1993; Borst et al., 1997). Ontologies are data models with two special characteristics, which lead to the notion of shared meaning or
semantics: 1. Ontologies build upon a shared understanding within a community. This understanding
represents an agreement of experts over the concepts
and relationships that are present in a domain. 2.
Ontologies use machine-processable representations
(expressed in formal languages such as RDF (Lassila
and Swick, 1999) and OWL (Dean et al., 2004)),
which allows computers to manipulate ontologies.
Ontologies have been widely applied in the context
of integration and representation of various knowledge resources in organizations (Berners-Lee et al.,
2001). Machine readable metadata and semantic web
are increasingly used to enhance the information access facility. Ontologies are the backbone of semantic
web which facilitates sharing and re-use of knowledge not only between software agents and computers but also between individuals (Fensel, 2001).
Ontologies to Support Knowledge Audit
Outcomes
Typical Outcomes from Knowledge Audit
Methodologies
Normally, after applying a knowledge audit methodology, the results are presented in a final report including knowledge inventory, knowledge maps, and
knowledge flows. After the inventory, maps and
flows are analyzed; it is possible to identify inefficiencies reflected in duplication of efforts, knowledge
gaps, knowledge barriers and knowledge-bottlenecks.
All this information is integrated in the final report
and it is presented to managers, including diverse
knowledge management initiatives. The knowledge
audit results represented only of this form has some
disadvantages as: inefficiency of searching specific
information about knowledge assets; and difficulty
of reusing them if a technological solution is needed
as a part of a knowledge management initiative.
Ontologies into Knowledge Audit
Methodologies
There are few proposes to apply ontologies in
knowledge audits methodologies, some of them are
only ideas and includes some aspects of auditing
phases.
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(Kingston, 2001) suggests an ontology that represents all the aspects of multi-perspective modeling.
The multi-perspective modeling idea is that for any
“knowledge asset” to be represented adequately, it’s
necessary to represent a number of different perspectives on its knowledge – and, possibly, to represent
the asset at multiple different levels of decomposition. That is, for any knowledge resource, it can
represent what it is, who possesses it, how it is used,
where it can be found, when it is needed and why it
exists (or why it is useful) (Zachman, 2002). In relation to levels of decomposition, Zachman illustrates
the different levels of abstraction using examples
from design and construction of a building, starting
from the “scope” level (which takes a “ballpark”
view on the building which is primarily the concern
of the architect, and may represent the gross sizing,
shape, and spatial relationships as well as the mutual
understanding between the architect and owner),
going through the “enterprise” level (primarily the
concern of the owner, representing the final building
as seen by the owner, and floor plans, based on architect’s drawings) and on through three other levels
(the “system” level, the “technology constrained”
level and the “detailed representation” level, respectively the concerns of the designer, the builder and
the subcontractor) before arriving at the “functioning
enterprise” level (in this example, the actual building). Zachman describes this framework as "a simple,
logical structure of descriptive representations for
identifying ‘models’ that are the basis for designing
the enterprise and for building the enterprise’s systems" (Zachman, 2002).
If knowledge is collected and indexed considering
the multi-perspective modeling and applying ontologies to represent it, should be possible to browse all
the people who possess a particular knowledge resource (or part of it); or all the knowledge resources
held by a particular person; or all the activities that
can be supported by a particular knowledge resource.
This approach is not included in any knowledge audit
methodology.
(Kingston, 2001) designed an approach to knowledge auditing which uses an ontology of knowledgerelated terms. The aim is to carry out a knowledge
audit of Artificial Intelligence research and researchers, and so the terms focus on research topics, publication details, and so on.
(Iazzolino and Pietrantonio, 2005) suggests in their
knowledge audit methodology where the first phase
is aiming at (a) analyzing the whole organizational
knowledge in all different forms and kinds and then
(b) at classifying these forms and kinds by the three
components of the enterprise intellectual capital: the
human capital, the structural capital and the relational
capital. The specific outcome to obtain behind this
phase is then a map of the entire organization's
knowledge that must be used for building-up a related descriptive frame of enterprise intangible assets
– the ontology-based schemes can be specifically
used to do this (Van Elst and Abecker, 2001).
The ontology-based scheme is not detailed nor
explained; additionally, the knowledge audit methology proposed doesn´t explain how to apply the ontology and the way to analyze and classify the total
organizational knowledge.
Necessity of a Framework to Structure
Knowledge Audit Outcomes
The main thing that makes knowledge difficult to
manage directly is a lack of some frame of reference
or an adequate representation scheme (Gordon,
2000). As a way of representing, sharing and reusing
organizational knowledge, the ontological discipline
acts as both a knowledge modeling language and
knowledge engineering technique (Vasconselos et
al., 2003).
There are important aspects to consider in an ontology-based framework to represent the knowledge
audit outcomes. These aspects are shown in figure
1.
PEREZ-SOLTERO, SANCHEZ-SCHMITZ, BARCELO-VALENZUELA, PALMA-MENDEZ, MARTIN-RUBIO
Fig. 1: Aspects to consider in an ontology-based framework to support knowledge audit outcomes
Applying an approach to represent knowledge audit
outcomes supported by ontologies it is possible to
solve the problem previously explained and exhibits
various aspects of knowledge audits outcomes at the
same time. Some of these aspects are knowledge inventory, knowledge flow, knowledge classification,
and knowledge valuation; as well a knowledge
management analysis; and the additional benefit of
knowledge reuse.
Some strategies could be applied to carry out these
objectives and are detailed in next section.
Important Aspects to Consider in an
Ontology-Based Framework
After analyzing diverse literature, we have found
feasible to apply a preliminary ontology-based
framework taking into account the important aspects
previously proposed. Next, the important aspects
proposed, different approaches that can be implemented and some practical examples are explained.
•
Knowledge Inventory: As a source of reference
to know what, who and where any asset of
knowledge is located. The double arrow between
knowledge inventory and ontological representation shown in figure 1, means, knowledge assets
could be represented in an ontology and searching on it, the knowledge inventory can be retrieved partially or totally.
The ontological approach uses ontologies to represent and manage both organisational knowledge
containers and contents (Vasconselos et al., 2003).
If knowledge is collected and is indexed according
to aspects of an ontology, that is, for any knowledge
resource, it can represent what it is, who possesses
it, how it is used, where it can be found, when it is
needed and why it exists (or why it is useful); then
it should be possible to browse all the people who
possess a particular knowledge resource (or part of
it); or all the knowledge resources held by a particular person; or all the activities that can be supported
by a particular knowledge resource (Kingston, 2001).
(Van and Abecker, 2001) state a related descriptive
frame of enterprise intangible assets – the ontologybased schemes can be specifically used to do this.
(Gualtieri and Ruffolo, 2005) propose an ontologybased framework called COKE (Core Organizational
Knowledge Entities). The COKE ontologies formally
represent human resources (represents individuals
working in the organization and social groups they
are involved in. Each individual profile is represented
in term of implicit, explicit, individual and social
knowledge, organizational role, social group membership, required technical resources), business processes (contains procedural knowledge related to the
managerial, operational and decisional processes.
Each of them is described in terms of activities, subprocesses, transition states and conditions, involved
actors, treated topics, etc.), knowledge objects (maps
the structure of logical objects, e. g. database schema,
database tables, textual documents, web pages, etc.;
containing explicit knowledge under structured,
semi-structured or unstructured form (AAAI, 2000)),
technical resources (identifies the tools by which
knowledge objects are created, acquired, stored and
retrieved) constituting the main elements characterizing the organizational structure and playing a fundamental role in business activities execution.
(Jackson, 2004) in his research has used a real life
consulting case study to show how the needs of organizations can be addressed by providing rigorous
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classifications of their knowledge as a basis for
knowledge storage and access on Intranets. The discussion space of ontologies was used in its philosophical and technological sense to provide a platform
of methods to try and provide a practical response
based upon theory.
•
Knowledge Flow: As a form to represent and retrieve information from the flow and its relation
with the rest of assets. The double arrow between
knowledge flow and ontological representation
shown in figure 1, means, flows could be represented in an ontology and searching on it information about knowledge flows can be retrieved
partially or totally.
Another use for an ontology is one that highlights
particular communities of practice. Such communities consist of groups of like-minded workers, possibly across companies and even technical sectors,
who share a number of assumptions (tacit knowledge) about their work, maybe focusing on particular
approaches or sub-disciplines. Traversing relationships in a valuation ontology, weighted to reflect
their importance (for valuation), can show links
between people. There are, of course, issues here
about how to draw boundaries around such communities, which relationships to track and how to weight
them, and how to calculate the strength of a connection. Nevertheless ontologies, by providing the conceptual apparatus to express the relationships, hierarchies and axioms that will be of importance to defining a community, can be of help in understanding
such communities, which often are difficult for
management to track because of their informal nature
(O’Hara and Shadbolt, 2001).
•
Knowledge Classification: As a source of reference to know characteristics and classification
of knowledge assets. The double arrow between
knowledge classification and ontological representation shown in figure 1, means, knowledge
classification could be represented in an ontology
and searching on it the classification can be retrieved partially or totally.
A Cost/benefit analysis for KM decisions such as
whether to codify or recodify a body of knowledge
could be highly valuable. An ontology for classifying
such bodies of knowledge in their organizational
context along value-relevant dimensions would be
applied, to provide an understanding of the value of
the knowledge within the business plan/processes of
the host organization, and provide useful pointers
towards a cost/benefit analysis of a proposed codification (O’Hara and Shadbolt, 2001).
Knowledge resources can be classified in different
ways in an ontology. (Weinberger, 2003) proposes
two subclasses: 1. Documents – structured repositories that include best practices, lessons learned, FAQ,
stories, guides, proposals and engagements, as well
as other documents forms, and 2. Pocket Items - accommodates soft knowledge “passing” in various
pipelines, such as bulletin boards, knowledge pockets
(expert heads), discussion groups, knowledge centers
and knowledge markets. Documents can be classified
as explicit, articulated, codified, concrete and source.
Pocket Items classified as tacit, subjective and intuitive.
•
Knowledge Valuation: As a form to value any
assets of knowledge. The double arrow between
knowledge valuation and ontological representation shown in figure 1, means, knowledge valuation could be represented in an ontology and
searching on it, the knowledge valuation can be
retrieved partially or totally.
The aim of a knowledge valuation ontology should
be to allow users to express factors relevant to valuing a particular piece of knowledge. Much of this,
of course, is an open question given that knowledge’s
non-marketability makes it very difficult to suggest
an objective value (O’Hara and Shadbolt, 2001).
Considering this difficult, however some researchers as O’Hara and Shadbolt state the more a piece
of knowledge is used, the more valuable it is, the
more likely it is to be embodied in and essential for
production processes. Hence we will want a knowledge valuation ontology to enable the expression of
the connectedness of a piece of knowledge or a
knowledge source with a network of users or community of practice. Using inference-supporting ontologies to understand the value of knowledge has the
potential to be an important tool for the management
of knowledge assets. We have seen what a large part
of a company’s value is down to the intangible assets,
and knowledge is one of the most important of those.
Knowing more about what it is worth is a key factor
in using it properly (O’Hara and Shadbolt, 2001).
Several parameters and combinations of parameters to knowledge valuation have been tested and the
following four have been found to be the most useful
in all audits. Importance (How important is the
knowledge to the company?), difficulty (How difficult would it be to replace this knowledge?),
study–experience (Is the knowledge acquired mainly
from study or practice?), known by (What proportion
of the staff in the knowledge area know this?).
Parameter values are estimates and can be subjective.
However some validation does occur during the interview process and it is important to inform managers that the parameters reflect what their staff is
thinking and if this is a problem then this may also
be something that requires attention (Gordon 2000).
These are some examples of parameters that could
PEREZ-SOLTERO, SANCHEZ-SCHMITZ, BARCELO-VALENZUELA, PALMA-MENDEZ, MARTIN-RUBIO
be used as attributes of an ontology to knowledge
valuation.
Many, if not all, knowledge asset instances will
have base indicators of value, which would be expressed as attributes of the instances. For example:
Publications might include an attribute quantifying
its citations, or the citation rates of the authors, and
the impact factor of the journal in which it was published. Individuals may have as attributes the ranking
of their institution, the number of publications or
patents for which they are responsible, the amount
of research funding for which they are responsible,
or simple indicators of rank (e.g. Professor, CEO)
(O’Hara and Shadbolt, 2001).
•
Knowledge Management Analysis: As a support
tool to analyze organizational knowledge management processes, detect problems/opportunities
and knowledge gaps found in the organization
to improve knowledge management in the organization. The arrow from ontological representation to knowledge management analysis shown
in figure 1, means the ontology could be examined to obtain the knowledge management
analysis considering the inventory, flows, classification and valuation.
The current state of knowledge within an organization is mapped, e.g. by creating a matrix of domain
problems crossed with known solutions/best practice.
Hence, such a matrix can be used to identify knowledge gaps, such as problems for which the organization possesses either no solution, or only unreliable
or expensive ones. For an organization with a large
library of knowledge sources, some automatic processing may help to link some problems with solutions. An ontology that relates knowledge sources
to particular people and processes within the organization could help cut down search spaces dramatically (O’Hara and Shadbolt, 2001).
•
Knowledge Reuse: As knowledge representation
strategy to reuse the results of the audit if a
technological solution is needed. The arrow from
ontological representation to knowledge reuse
shown in figure 1, means the ontology provides
a collective understanding of a domain in a way
to facilitate knowledge sharing and reuse between
software agents and computers but also between
individuals.
Ontologies to reuse knowledge in a technological
issue are very important. At present, the greatest
needs are in the areas of integration, standardization,
development of tools, and adoption by users (Antoniou and Harmelen, 2004). A strategy to satisfy this
necessity has been developing software applications
using Web technologies. Some of the audit outcomes
could be available in a web-based system. From this
point of view, the next development step should be
centered on Semantic Web vision. (Antoniou and
Harmelen, 2004) state the goal of the Semantic Web
is to assist human users in their day-to-day online
activities. In the context of the Web, ontologies
provide a shared understanding of a domain. Such a
shared understanding is necessary to overcome differences in terminology. It is easy to see that ontologies support semantic interoperability.
The ontological approach uses ontologies to represent and manage both organizational knowledge
containers and contents. This technique allows the
representation of organizational knowledge in a way
that facilitates knowledge sharing and reuse between
organizational agents (Vasconselos et al., 2003).
(Gualtieri and Ruffolo, 2005) sample that a
Knowledge Management System must be able to
support the generation, discovery, capture, store,
distribution and application of a wide variety of
knowledge (i.e. explicit knowledge under structured,
semi-structured and unstructured forms and individual and social aspects of implicit knowledge) through
related knowledge-based services. Moreover, a
Knowledge Management System needs capability
to interoperate with already existing organizational
information systems. To satisfy these requirements
a Knowledge Management System needs knowledge
representation capabilities that can be provided by
ontology languages, able to allow the specification
of the different organizational knowledge forms and
kinds and to carry out an abstract representation of
organizational entity supporting interoperability
among different systems and organizational areas.
For example (Sridharan et al., 2004) propose a
framework for a knowledge management system
which uses ontology to enable efficient reuse and
sharing of knowledge in a web based learning environment.
Discussion
After analysing the literature related to utilize ontologies in knowledge audit methodologies, we have
found few cases of its application. There are cases
where ontologies are applied in some phases. Explicitly, not any ontology is applied as an integral
knowledge representation strategy to represent their
outcomes, however it exists evidence of benefits in
activities related to knowledge inventory, knowledge
flow, knowledge classification, knowledge valuation,
knowledge management analysis and knowledge
reuse. Considering the aspects of the ontology-based
framework to support knowledge audit outcomes
proposed and illustrated in figure 1, it is possible to
represent the knowledge inventory, knowledge flow,
knowledge classification and knowledge valuation;
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additionally, if the ontology is developed considering
these aspects, it will be possible to obtain the inventory, flow, classification and knowledge valuation of
the organizational knowledge assets partially or
totally. The execution of a query to the ontology can
be executed using a specific tool to retrieve all the
elements related with a specific concept. For example
a query result can contain people knowing a given
concept or systems containing knowledge objects
related to some concepts.
Further, if the ontology is examined a KM analysis
to detect problems/opportunities and knowledge gaps
found in the organization might be obtained to improve KM into organization. Finally, the ontology
would be a good scheme to reuse the results of the
knowledge audit if a technological solution is needed.
This would allow a management of the tacit and explicit knowledge stored in structured, semistructured
or unstructured machine-readable form.
Conclusions and Future Work
This paper demonstrates and analyzes the importance
of using ontologies as a strategy to represent formally
knowledge audit results. The evidence in literature
was found and shows benefits in issues related to
knowledge inventory, knowledge flow, knowledge
classification, knowledge valuation, knowledge
management analysis and knowledge reuse. Considering these aspects, a preliminary integral ontology-
based framework to support knowledge audit outcomes is proposed.
Additionally, applying this approach the following
benefits could be obtained: a support tool to detect
problems/opportunities found in the organization to
improve KM; the results of the audit can be reused
if a technological solution is needed; as a source of
reference to know what, where, characteristics,
classification and value of any assets of knowledge;
as a form to represent the flow and its relation with
the rest of assets; an efficient way to retrieve information from knowledge inventory and/or knowledge
flows and automatically to know the impact and relation with the rest of the knowledge assets.
There are different activities to be carried out in
future work. Some of them are related detailing the
ontology structure in terms of classes, attributes and
relations in each of the aspects of the ontology-based
framework proposed to support knowledge audit
outcomes. Later, it will be validated in a test case.
Perhaps applying a software tool to model the ontology could be a good strategy to facilitate the validation process.
Acknowledgments
To the Universidad de Sonora and to ‘Programa de
Mejoramiento al Profesorado PROMEP’ for the
support of this research and the development of the
Information Technologies Academic Group.
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About the Authors
Prof. Alonso Perez-Soltero
Alonso Perez-Soltero Professor at the Department of Industrial Engineering in University of Sonora, Mexico.
Prof. Gerardo Sanchez-Schmitz
Gerardo Sanchez-Schmitz Professor at the Department of Industrial Engineering in University of Sonora,
Mexico.
51
52
INTERNATIONAL JOURNAL OF TECHNOLOGY, KNOWLEDGE AND SOCIETY, VOLUME 2
Prof. Mario Barcelo-Valenzuela
Mario Barcelo-Valenzuela Professor at the Department of Industrial Engineering in University of Sonora,
Mexico.
Dr. Jose Tomas Palma-Mendez
Jose Tomas Palma-Mendez Doctor at the Department of Information & Communication Engineering in University
of Murcia
Dr. Fernando Martin-Rubio
Fernando Martin-Rubio Doctor at the Department of Information & Communication Engineering in University
of Murcia
THE INTERNATIONAL JOURNAL OF TECHNOLOGY, KNOWLEDGE AND SOCIETY
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